Knowledge · MiniMax

MiniMax-Text Base on MMLU-Pro: score, rank and what it means

MiniMax-Text Base scores 77.1 on MMLU-Pro, placing it #273 of 600 models in the index. The benchmark average is 73.5 and the leader is GPT-5.2 Pro at 91.6.

Score
77.1
0–100 scale
Rank
#273
of 600
Percentile
55th
within the index
Benchmark avg
73.5
all models
Leader
91.6
GPT-5.2 Pro
Gap to leader
14.5
points

Score distribution

The chart shows the top 10 models on MMLU-Pro, with MiniMax-Text Base highlighted. Longer bars mean higher scores.

GPT-5.2 Pro 91.6
GPT-5.1 Max 91.3
Claude Opus 4.5 Pro 91.2
Claude Opus 4.5 Reasoning 90.7
Gemini 3 Pro Reasoning 90.1
GPT-5.1 Reasoning 90.0
GPT-5.2 89.9
Claude Opus 4.5 Max 89.5
GPT-5.2 Vision 89.5
Gemini 3 Pro Max 89.5

Top 10 leaderboard

#ModelProviderContextInput / 1MAvg score
1 GPT-5.2 Pro OpenAI 2000000 $21.32 89.6
2 GPT-5.1 Max OpenAI 2000000 $20.50 87.9
3 Claude Opus 4.5 Pro Anthropic 200000 $20.98 89.1
4 Claude Opus 4.5 Reasoning Anthropic 2000000 $20.91 88.2
5 Gemini 3 Pro Reasoning Google DeepMind 200000 $19.69 87.5
6 GPT-5.1 Reasoning OpenAI 400000 $20.27 88.3
7 GPT-5.2 OpenAI 2000000 $19.71 88.0
8 Claude Opus 4.5 Max Anthropic 1000000 $20.90 87.8
9 GPT-5.2 Vision OpenAI 1000000 $19.03 87.4
10 Gemini 3 Pro Max Google DeepMind 2000000 $20.57 87.9

What this score means

On MMLU-Pro, MiniMax-Text Base sits at #273 of 600. The leader, GPT-5.2 Pro, scores 91.6, so the gap to the top is 14.5 points. The median model in this index scores 76.1, which puts MiniMax-Text Base above the midpoint.

MiniMax models average 72.7 on this benchmark. MiniMax-Text Base is 4.4 points above that provider line, so it is a stronger result than the average MiniMax model.

Among multimodal models, the average is 80.0. MiniMax-Text Base is 2.9 points below that group. This is the more useful comparison if you are shortlisting within the same tier rather than across the whole index.

Read a 77.1 as a relative signal, not an absolute guarantee. Benchmark scores depend on prompt format, evaluation harness and contamination controls. Use this page to narrow the shortlist, then test the model on your own data.

Price context

At $3.66 per million input tokens, MiniMax-Text Base is below the median input price of $4.00 across the 600 models scored on MMLU-Pro. The models immediately around it in the ranking are shown below so you can compare score and price together.

ModelProviderContextInput / 1MAvg score
Mixtral Mistral AI 65536 $3.48 73.7
Yi-Lightning Chat 01.AI 32768 $3.79 75.5
Nemotron 4 Chat NVIDIA 65536 $3.99 75.0
Nova Lite Max Amazon 200000 $9.79 73.4
MiniMax-Text Base MiniMax 128000 $3.66 74.9
Yi-Large 01.AI 32768 $3.40 73.5
Claude Haiku 4.5 Mini Anthropic 16384 $8.76 71.6

Other benchmarks for MiniMax-Text Base

BenchmarkScoreRankPercentileRelative
GPQA Diamond 68.4 307 49th
SWE-bench Verified 62.2 276 54th
HumanEval 79.9 236 61th
MATH-500 79.6 219 64th
AIME 2025 73.6 195 68th
LiveCodeBench 68.0 226 62th
IFEval 82.8 250 58th
TruthfulQA 75.7 316 47th
HellaSwag 86.2 209 65th
MMMU 70.1 299 50th
Arena Elo 1373.6 304 49th

FAQ

What score does MiniMax-Text Base get on MMLU-Pro?

MiniMax-Text Base scores 77.1 on MMLU-Pro in this index. The benchmark average is 73.5 and the top score is 91.6.

How does MiniMax-Text Base rank on MMLU-Pro?

It ranks #273 of 600 models, in the 55th percentile. The leader is GPT-5.2 Pro at 91.6.

Is MiniMax-Text Base good at MMLU-Pro?

Yes, relative to the index: it is above the median and inside the top half of scores. The result should be read alongside its price and context window.

How is the MMLU-Pro score calculated?

MMLU-Pro is a knowledge benchmark. Scores in this index are modelled estimates on a 0–100 scale (or Elo for Arena), derived from capability tier and benchmark difficulty. They are not official leaderboard results.

What does MiniMax-Text Base cost?

MiniMax-Text Base costs $3.66 per million input tokens and $19.71 per million output tokens.

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